{
 "metadata": {
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  "signature": "sha256:2369d14957706c3b5aad13841d346a4936056451df78e97ac6d239b7e83c13c3"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import pandas as pd\n",
      "import matplotlib.pyplot as plt\n",
      "import seaborn as sns\n",
      "plt.style.use('fivethirtyeight')\n",
      "import warnings\n",
      "warnings.filterwarnings('ignore')\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = pd.read_csv(\"train.csv\")\n",
      "data.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<style scoped>\n",
        "    .dataframe tbody tr th:only-of-type {\n",
        "        vertical-align: middle;\n",
        "    }\n",
        "\n",
        "    .dataframe tbody tr th {\n",
        "        vertical-align: top;\n",
        "    }\n",
        "\n",
        "    .dataframe thead th {\n",
        "        text-align: right;\n",
        "    }\n",
        "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>PassengerId</th>\n",
        "      <th>Survived</th>\n",
        "      <th>Pclass</th>\n",
        "      <th>Name</th>\n",
        "      <th>Sex</th>\n",
        "      <th>Age</th>\n",
        "      <th>SibSp</th>\n",
        "      <th>Parch</th>\n",
        "      <th>Ticket</th>\n",
        "      <th>Fare</th>\n",
        "      <th>Cabin</th>\n",
        "      <th>Embarked</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>3</td>\n",
        "      <td>Braund, Mr. Owen Harris</td>\n",
        "      <td>male</td>\n",
        "      <td>22.0</td>\n",
        "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>A/5 21171</td>\n",
        "      <td>7.2500</td>\n",
        "      <td>NaN</td>\n",
        "      <td>S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>2</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
        "      <td>female</td>\n",
        "      <td>38.0</td>\n",
        "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>PC 17599</td>\n",
        "      <td>71.2833</td>\n",
        "      <td>C85</td>\n",
        "      <td>C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>3</td>\n",
        "      <td>1</td>\n",
        "      <td>3</td>\n",
        "      <td>Heikkinen, Miss. Laina</td>\n",
        "      <td>female</td>\n",
        "      <td>26.0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>STON/O2. 3101282</td>\n",
        "      <td>7.9250</td>\n",
        "      <td>NaN</td>\n",
        "      <td>S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>4</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
        "      <td>female</td>\n",
        "      <td>35.0</td>\n",
        "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>113803</td>\n",
        "      <td>53.1000</td>\n",
        "      <td>C123</td>\n",
        "      <td>S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>5</td>\n",
        "      <td>0</td>\n",
        "      <td>3</td>\n",
        "      <td>Allen, Mr. William Henry</td>\n",
        "      <td>male</td>\n",
        "      <td>35.0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>373450</td>\n",
        "      <td>8.0500</td>\n",
        "      <td>NaN</td>\n",
        "      <td>S</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "   PassengerId  Survived  Pclass  \\\n",
        "0            1         0       3   \n",
        "1            2         1       1   \n",
        "2            3         1       3   \n",
        "3            4         1       1   \n",
        "4            5         0       3   \n",
        "\n",
        "                                                Name     Sex   Age  SibSp  \\\n",
        "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
        "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
        "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
        "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
        "4                           Allen, Mr. William Henry    male  35.0      0   \n",
        "\n",
        "   Parch            Ticket     Fare Cabin Embarked  \n",
        "0      0         A/5 21171   7.2500   NaN        S  \n",
        "1      0          PC 17599  71.2833   C85        C  \n",
        "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
        "3      0            113803  53.1000  C123        S  \n",
        "4      0            373450   8.0500   NaN        S  "
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print (data.shape[0])\n",
      "data.isnull().sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "891\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "PassengerId      0\n",
        "Survived         0\n",
        "Pclass           0\n",
        "Name             0\n",
        "Sex              0\n",
        "Age            177\n",
        "SibSp            0\n",
        "Parch            0\n",
        "Ticket           0\n",
        "Fare             0\n",
        "Cabin          687\n",
        "Embarked         2\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<style scoped>\n",
        "    .dataframe tbody tr th:only-of-type {\n",
        "        vertical-align: middle;\n",
        "    }\n",
        "\n",
        "    .dataframe tbody tr th {\n",
        "        vertical-align: top;\n",
        "    }\n",
        "\n",
        "    .dataframe thead th {\n",
        "        text-align: right;\n",
        "    }\n",
        "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>PassengerId</th>\n",
        "      <th>Survived</th>\n",
        "      <th>Pclass</th>\n",
        "      <th>Age</th>\n",
        "      <th>SibSp</th>\n",
        "      <th>Parch</th>\n",
        "      <th>Fare</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td>891.000000</td>\n",
        "      <td>891.000000</td>\n",
        "      <td>891.000000</td>\n",
        "      <td>714.000000</td>\n",
        "      <td>891.000000</td>\n",
        "      <td>891.000000</td>\n",
        "      <td>891.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td>446.000000</td>\n",
        "      <td>0.383838</td>\n",
        "      <td>2.308642</td>\n",
        "      <td>29.699118</td>\n",
        "      <td>0.523008</td>\n",
        "      <td>0.381594</td>\n",
        "      <td>32.204208</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>257.353842</td>\n",
        "      <td>0.486592</td>\n",
        "      <td>0.836071</td>\n",
        "      <td>14.526497</td>\n",
        "      <td>1.102743</td>\n",
        "      <td>0.806057</td>\n",
        "      <td>49.693429</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.420000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>223.500000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>2.000000</td>\n",
        "      <td>20.125000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>7.910400</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>446.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>3.000000</td>\n",
        "      <td>28.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>14.454200</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td>668.500000</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>3.000000</td>\n",
        "      <td>38.000000</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>31.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td>891.000000</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>3.000000</td>\n",
        "      <td>80.000000</td>\n",
        "      <td>8.000000</td>\n",
        "      <td>6.000000</td>\n",
        "      <td>512.329200</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
        "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
        "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
        "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
        "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
        "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
        "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
        "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
        "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
        "\n",
        "            Parch        Fare  \n",
        "count  891.000000  891.000000  \n",
        "mean     0.381594   32.204208  \n",
        "std      0.806057   49.693429  \n",
        "min      0.000000    0.000000  \n",
        "25%      0.000000    7.910400  \n",
        "50%      0.000000   14.454200  \n",
        "75%      0.000000   31.000000  \n",
        "max      6.000000  512.329200  "
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f, ax = plt.subplots(1, 2, figsize=(18,8))\n",
      "data['Survived'].value_counts().plot.pie(explode=[0,0.1], autopct='%1.1f%%',ax=ax[0], shadow=True)\n",
      "ax[0].set_title('Survived')\n",
      "sns.countplot('Survived',data=data, ax=ax[1])\n",
      "ax[1].set_title('Survived')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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MZsLW3VlnkiRl6r2RL4zJOoQkSZK0LhZDQ+vlQDsAu71mTxoa/POVpPo2Bjgu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\nurMCOHXheDz0xuHO5ffuaaoJxa1P0kJmCQEAAACZjWIIjvA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       "text": [
        "<matplotlib.figure.Figure at 0x7fc44d487dd8>"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.groupby(['Sex','Survived'])['Survived'].count()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 39,
       "text": [
        "Sex     Survived\n",
        "female  0            81\n",
        "        1           233\n",
        "male    0           468\n",
        "        1           109\n",
        "Name: Survived, dtype: int64"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f, ax = plt.subplots(1, 2, figsize=(18, 8))\n",
      "data[['Sex','Survived']].groupby(['Sex']).mean().plot.bar(ax=ax[0])\n",
      "ax[0].set_title('Survived vs Sex')\n",
      "sns.countplot('Sex', hue='Survived', data=data, ax=ax[1])\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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5Kfu+p8fnf0rz+QcAYDIYyXKz65IMVNVWVbVekqOTLBjbsgAAAAAY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\nSs7AmVqFAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fc44d452080>"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.crosstab(data.Pclass, data.Survived, margins=True).style.background_gradient(cmap='summer_r')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<style  type=\"text/css\" >\n",
        "    #T_ebffb3ae_219a_11ea_878b_4a77c9c954e3row0_col0 {\n",
        "            background-color:  #ffff66;\n",
        "        }    #T_ebffb3ae_219a_11ea_878b_4a77c9c954e3row0_col1 {\n",
        "            background-color:  #cee666;\n",
        "        }    #T_ebffb3ae_219a_11ea_878b_4a77c9c954e3row0_col2 {\n",
        "            background-color:  #f4fa66;\n",
        "        }    #T_ebffb3ae_219a_11ea_878b_4a77c9c954e3row1_col0 {\n",
        "            background-color:  #f6fa66;\n",
        "        }    #T_ebffb3ae_219a_11ea_878b_4a77c9c954e3row1_col1 {\n",
        "            background-color:  #ffff66;\n",
        "        }    #T_ebffb3ae_219a_11ea_878b_4a77c9c954e3row1_col2 {\n",
        "            background-color:  #ffff66;\n",
        "        }    #T_ebffb3ae_219a_11ea_878b_4a77c9c954e3row2_col0 {\n",
        "            background-color:  #60b066;\n",
        "        }    #T_ebffb3ae_219a_11ea_878b_4a77c9c954e3row2_col1 {\n",
        "            background-color:  #dfef66;\n",
        "        }    #T_ebffb3ae_219a_11ea_878b_4a77c9c954e3row2_col2 {\n",
        "            background-color:  #90c866;\n",
        "        }    #T_ebffb3ae_219a_11ea_878b_4a77c9c954e3row3_col0 {\n",
        "            background-color:  #008066;\n",
        "        }    #T_ebffb3ae_219a_11ea_878b_4a77c9c954e3row3_col1 {\n",
        "            background-color:  #008066;\n",
        "        }    #T_ebffb3ae_219a_11ea_878b_4a77c9c954e3row3_col2 {\n",
        "            background-color:  #008066;\n",
        "        }</style>  \n",
        "<table id=\"T_ebffb3ae_219a_11ea_878b_4a77c9c954e3\" > \n",
        "<thead>    <tr> \n",
        "        <th class=\"index_name level0\" >Survived</th> \n",
        "        <th class=\"col_heading level0 col0\" >0</th> \n",
        "        <th class=\"col_heading level0 col1\" >1</th> \n",
        "        <th class=\"col_heading level0 col2\" >All</th> \n",
        "    </tr>    <tr> \n",
        "        <th class=\"index_name level0\" >Pclass</th> \n",
        "        <th class=\"blank\" ></th> \n",
        "        <th class=\"blank\" ></th> \n",
        "        <th class=\"blank\" ></th> \n",
        "    </tr></thead> \n",
        "<tbody>    <tr> \n",
        "        <th id=\"T_ebffb3ae_219a_11ea_878b_4a77c9c954e3level0_row0\" class=\"row_heading level0 row0\" >1</th> \n",
        "        <td id=\"T_ebffb3ae_219a_11ea_878b_4a77c9c954e3row0_col0\" class=\"data row0 col0\" >80</td> \n",
        "        <td id=\"T_ebffb3ae_219a_11ea_878b_4a77c9c954e3row0_col1\" class=\"data row0 col1\" >136</td> \n",
        "        <td id=\"T_ebffb3ae_219a_11ea_878b_4a77c9c954e3row0_col2\" class=\"data row0 col2\" >216</td> \n",
        "    </tr>    <tr> \n",
        "        <th id=\"T_ebffb3ae_219a_11ea_878b_4a77c9c954e3level0_row1\" class=\"row_heading level0 row1\" >2</th> \n",
        "        <td id=\"T_ebffb3ae_219a_11ea_878b_4a77c9c954e3row1_col0\" class=\"data row1 col0\" >97</td> \n",
        "        <td id=\"T_ebffb3ae_219a_11ea_878b_4a77c9c954e3row1_col1\" class=\"data row1 col1\" >87</td> \n",
        "        <td id=\"T_ebffb3ae_219a_11ea_878b_4a77c9c954e3row1_col2\" class=\"data row1 col2\" >184</td> \n",
        "    </tr>    <tr> \n",
        "        <th id=\"T_ebffb3ae_219a_11ea_878b_4a77c9c954e3level0_row2\" class=\"row_heading level0 row2\" >3</th> \n",
        "        <td id=\"T_ebffb3ae_219a_11ea_878b_4a77c9c954e3row2_col0\" class=\"data row2 col0\" >372</td> \n",
        "        <td id=\"T_ebffb3ae_219a_11ea_878b_4a77c9c954e3row2_col1\" class=\"data row2 col1\" >119</td> \n",
        "        <td id=\"T_ebffb3ae_219a_11ea_878b_4a77c9c954e3row2_col2\" class=\"data row2 col2\" >491</td> \n",
        "    </tr>    <tr> \n",
        "        <th id=\"T_ebffb3ae_219a_11ea_878b_4a77c9c954e3level0_row3\" class=\"row_heading level0 row3\" >All</th> \n",
        "        <td id=\"T_ebffb3ae_219a_11ea_878b_4a77c9c954e3row3_col0\" class=\"data row3 col0\" >549</td> \n",
        "        <td id=\"T_ebffb3ae_219a_11ea_878b_4a77c9c954e3row3_col1\" class=\"data row3 col1\" >342</td> \n",
        "        <td id=\"T_ebffb3ae_219a_11ea_878b_4a77c9c954e3row3_col2\" class=\"data row3 col2\" >891</td> \n",
        "    </tr></tbody> \n",
        "</table> "
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 52,
       "text": [
        "<pandas.io.formats.style.Styler at 0x7fc44cbf4b38>"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sns.factorplot('Pclass', 'Survived', hue='Sex', data=data)\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Furz9GhUlIp2ErBDVxcTBOuVpsNHH/KzdCsuspwFrpXZ1iYglIStEDUqbTrDddL/PNroj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\nKxFJc/tCaEmrkWx/AHuZOYe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       "text": [
        "<matplotlib.figure.Figure at 0x7fc44d1ba278>"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}